How a Data Lake, Data Warehouse, and Rayven work together

A data warehouse, data lake, and integrated data, AI + IoT platforms (like Rayven’s) each offer distinct but complementary capabilities in managing and analysing data.

A data warehouse is a system used for reporting and data analysis, structured to provide a fast and efficient method of retrieving data for business intelligence activities. It's often organised in a way to suit particular types of analysis and to support predefined business needs, therefore the data in a data warehouse is typically cleaned, transformed, and catalogued so that it is available for use in reports and dashboards.

Data warehouses are used by: End-users.
Capabilities data warehouses deliver: Data visualization, dashboards, BI, and data analytics.
Data warehouse examples: Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics, IBM Db2 Warehouse, and Oracle Autonomous Data Warehouse.

A data lake, on the other hand, is a vast pool of raw data stored in its native format until it's needed. This means it delivers more flexibility than a data warehouse, as you can dump any type and amount of data (structured, semi-structured, or unstructured) into a data lake without having to first define its structure (this gets done when it’s called). This makes it a good storage system for Big Data and real-time analytics.

Data lakes are used by: Data Scientists and Engineers.
Capabilities data lakes deliver: Predictive analytics, machine learning, data visualization, BI, and big data analytics.
Data lake examples: Amazon S3, Microsoft Azure Data Lake Storage, Google Cloud Storage, IBM Cloud Object Storage, Cloudera Data Platform, Databricks, and Snowflake.

A data, AI + IoT platform is not a purpose-built data storage system like a data warehouse or data lake (although it can perform this function). Instead, it is a platform that enables you to connect to multiple data sources (like data warehouses, data lakes, IoT devices, business systems, etc.), integrate and cleanse data (using set logic, machine learning and manual methods), apply advanced real-time and predictive analytics, and also to develop custom industrial applications and IoT solutions that put insights and Industry 4.0 capabilities in the hands of personnel, anywhere and in the moment. It can pull data from your data warehouse and data lake, analyse it, and provide real-time insights or predictions. In other words, Rayven helps you make sense of and act upon the data stored in your data warehouses and data lakes.

Data, AI + IoT platforms are used by: Data Scientists, Engineers, management, and end-users.
Capabilities Data, AI + IoT platforms deliver: Application development, predictive analytics, automation, machine learning, AI, data visualization, dashboards, BI, and Big Data analytics.
Data, AI + IoT platform examples: C3 AI Suite, Siemens MindSphere, and Rayven.

Data Warehouse, Data Lakes, and Integrated Data, AI + IoT platforms work best together.

The three components (and you can even throw in a data lakehouse into the mix) work together to form a comprehensive, hybrid data strategy. For example, your data lake could be used for storing raw data from various sources, your data warehouse could hold cleaned and transformed data for predefined analysis, and Rayven could be used to connect to these sources, apply advanced analytics, and provide real-time insights and solutions to meet your business challenges.

In essence, while data warehouses and data lakes are more about storage and organisation of data, Rayven is about actionability - taking that data and turning it into actionable, real-time, and readily available insights and infield actions through advanced analytics and applications with Industry 4.0 capabilities that anyone in a business can use, simply.

Speak to us today to discuss your needs and explore how our integrated data, AI + IoT platform can enable you to transform how you or your customers use real-time data and enable you to extract much, much more value from it, simply.